'''Trains a simple deep NN on the MNIST dataset.
Gets to 98.40% test accuracy after 20 epochs
(there is *a lot* of margin for parameter tuning).
2 seconds per epoch on a K520 GPU.
'''

import keras

from keras.datasets import mnist

from keras.models import Sequential

from keras.layers import Dense, Dropout

batch_size = 128

num_class = 10

epochs = 20

(x_train, y_train), (x_test, y_test) = mnist.load_data()
# 查看数据结构
print(x_test.shape)

# print(x_train)


x_train = x_train.reshape(60000, 784)

x_test = x_test.reshape(10000, 784)

# 把整数转换成小数
x_train = x_train.astype('float32')

x_test = x_test.astype("float32")

x_train = x_train / 255

x_test = x_test / 255

print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices


y_train = keras.utils.to_categorical(y_train, num_class)

y_test = keras.utils.to_categorical(y_test, num_class)

model = Sequential()

model.add(Dense(512, activation='relu', input_shape=(784,)))

model.add(Dropout(0.2))

model.add(Dense(512, activation='relu'))

model.add(Dropout(0.2))

model.add(Dense(num_class, activation='softmax'))

model.summary()

model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])

history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test))

score = model.evaluate(x_test, y_test, verbose=0)

print('Test loss:', score[0])
print('Test accuracy:', score[1])
